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Record W4200118076 · doi:10.1093/ofid/ofab466.573

372. Detection of SARS-CoV-2 RNAemia in Deceased Tissue Donors

2021· article· en· W4200118076 on OpenAlexaboutno aff
Melissa A. Greenwald, Eduard Grebe, Valerie Green, Alyce Linthurst Jones, Philip T. F. Williamson, Michael P. Busch, Matthew J. Kuehnert

Bibliographic record

VenueOpen Forum Infectious Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineViremiaNucleic acid testAntibodySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)NatVirologyConfidence intervalTransmission (telecommunications)Internal medicineCoronavirus disease 2019 (COVID-19)ImmunologyDiseaseInfectious disease (medical specialty)

Abstract

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Abstract Background Tissue donors are evaluated for communicable disease in order to minimize the risk of transmission to recipients. Although there are data suggesting SARS-CoV-2 viremia across a wide spectrum of illness, prevalence in deceased tissue donors and the potential for transplant transmission are unknown. Methods Eight tissue banks participated in a retrospective analysis of samples from eligible deceased tissue donors from Oct 2019 through June 2020, one participant in Canada and the remainder located in the United States. All four Census regions of the continental US and all major racial-ethnic groups were represented. EDTA or sodium citrate plasma aliquots were tested in singlicate with the Research Use Only Procleix SARS-CoV-2 Assay on the Procleix Panther System, which uses transcription-mediated nucleic acid amplification (TMA) technology for detection of the SARS-CoV-2 RNA. Plasma (or if unavailable, serum) aliquots were sent to Grifols for an alternate SARS-CoV-2 nucleic acid amplification (NAT) test to verify reactivity and also sent for antibody testing using the emergency use authorization Ortho VITROS Immunodiagnostic Products Anti-SARS-CoV-2 Total test. The VITROS assay uses immunometric technology for qualitative measurement of total antibody (IgG, IgA and IgM) to SARS-CoV-2. The proportion of donors with confirmed RNAemia (i.e., presence of SARS-CoV-2 RNA in plasma or serum) and 95% confidence intervals were computed. Results Of 3,455 donor samples with valid final results, 26 (0.76%) were initially positive for SARS-CoV-2 RNA; of these, 3 were confirmed by alternate NAT. Of donor samples collected in 2019 0.00% (95% CI: 0.00%,0.43%) were confirmed RNAemic, while of those collected in 2020, 0.12% (0.04%,0.34%) were confirmed RNAemic. One of 26 initial positive, and none of the three samples confirmed by alternate NAT, tested positive for anti-SARS-CoV-2 Spike antibodies by serology. Infectivity studies are pending on one sample with sufficient available volume. Conclusion The rate of SARS-CoV-2 RNAemia in deceased tissue donors is approximately 1 per 1,000, and it is unknown whether this RNAemia reflects the presence of infectious virus. Given these results, the risk of transmission through tissue is most likely to be low. Disclosures Melissa Greenwald, MD, Alamo Biologics (Consultant)Eurofins VRL Laboratories (Consultant)Right Cell Biologics (Consultant, Consultant Medical Director) Eduard Grebe, PhD, Gilead Sciences (Consultant)Sedia Biosciences Corporation (Consultant, Grant/Research Support)Vitalant (Employee) Alyce Linthurst Jones, PhD, LifeNet Health (Employee) Matthew Kuehnert, MD, American Association of Tissue Banks (Board Member)ICCBBA (Board Member)Musculoskeletal Transplant Foundation (Employee)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.363
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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